The school-to-prison-pipeline (STPP) has been a problem since long before any recent election. Likewise ICE raids leading to indefinite detentions and deportations of youth and their families have been a problem since at least the 1990s.

Nevertheless, there are two points to consider in the present moment. Disproportionate to their numbers, many African American and Latinx children are subject to persistent police abuse in the course of attending school. This is a key civil rights issue that must be corrected through retraining superintendents, principals, teachers, and others so that restorative justice practices can be the first resort, as opposed to criminalization of youth of color. Students who are black/Afro-Latinx and undocumented immigrants face double the risk.

One, pushing young people into the school-to-prison pipeline destroys their chances to graduate on time (if at all), with future harsh consequences for those with a criminal record, time spent in juvenile detention, and lack of education attempting to find work in an intolerant economy. This is how youth of color face stacked odds and an unforgiving society. Young people may be subjected to violence and additional abuse by peers or adults in juvenile detention centers. The loss of human potential is incalculable for the young person and her or his family.

Two, young people pushed out of schools also have a secondary effect on the overall health of the school: fewer students attending means depopulation and defunding for the students who remain. These schools are often already in underresourced communities where few businesses form a tax base, schools experience neglect of facilities and equipment, and teacher retention or turnover are often major hurdles. Already on precarious ground, these schools risk closure.

This is why it has always been imperative to halt the school-to-prison pipeline as quickly as possible. Principals MUST form the first line of defense to ensure that discretionary discipline addresses root needs, that ALL supports — including emotional and mental health — are given to a child of color in need, and they MUST work to carefully instill reparative, and not punitive, measures at the school level whenever possible. Again, reliance on School Resource Officers or city police must be the last resort reserved for cases where the child has broken a law.

Principals who call in police assistance for students’ minor behavorial issues are 1) activating, and not eliminating, the STPP, 2) in doing so they are participating in the eventual diminishment and depopulation of their own school. There should be a moratorium on calls to police and or SRO calls for all grade school children.

See what students and families can do with People’s Response Team Chicago‘s  CopWatch and Bystander training to ensure that IF police are called to a public school, communities can ensure that the police act in ways that  protect the rights of the child. These bystanders might be teachers, staff, parents, community members, or other students.

Here are also some resources for school leaders and administrators seeking alternatives to incarcerating youthful offenders. Community-based alternatives are key, so are school-based restorative justice programs.

“We’re In It For The Long Haul: Alternatives to Incarceration For Youth In Conflict With The Law,” IssueLab


Stamp Collectors Catalogue

Search: bird
(646 images found)

Fish Eagle
Fish Eagle, Zambia, stamp, bird, bird of prey, Postage, 4 n, Haliaeetus vocifer

Title: Fish Eagle
Location: Zambia
Description: African Fish Eagle [Haliaeetus vocifer] in a tree with a freshley caught fish
Keywords: stamp, bird, bird of prey, Postage, 4 n, Haliaeetus vocifer

Yellow-tailed Thornbill
Yellow-tailed Thornbill, Australia, stamp, bird, 5 c, Acanthiza chrysorrhoa
Galah, Australia, stamp, bird, 15 c, Eolophus roseicapilla
Scarlet Robin
Scarlet Robin, Australia, stamp, bird, 25 c, Petroica boodang
Pink Cockatoo
Pink Cockatoo, Australia, stamp, bird, $1.20, Lophochroa leadbeateri
Red-necked Avocet
Red-necked Avocet, Australia, stamp, bird, 13 c, Recurvirostra novaehollandiae
Golden Whistler on Pink
Golden Whistler on Pink, Australia, stamp, pink, bird, 20 c, Pachycephala pectoralis
Straw-necked Ibis
Straw-necked Ibis, Australia, stamp, bird, 30 c, Threskiornis spinicollis
Flame Robin
Flame Robin, Australia, stamp, bird, 50 c, nesting, Petroica phoenicea
Nesting Masked Woodswallow
Nesting Masked Woodswallow, Australia, stamp, bird, 45 c, Artamus personatus
Pied Oystercatcher
Pied Oystercatcher, Australia, stamp, bird, nesting, 30 c, Haematopus longirostris
Crimson finch
Crimson finch, Australia, stamp, 2 c, bird, K Breeden Williams, Neochmia phaeton
Eastern yellow robin
Eastern yellow robin, Australia, stamp, bird, K Breeden Williams, Eastern yellow robin, 20 c, Eopsaltria australis, RBA

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The temptation to buy a new cookbook is nigh irresistable sometimes.

You would think that a couple who thinks they have copious cookbooks would not have any issues finding a good recipe for a new dish. Or, at least, a dish that we don’t have a lot of experience with.

All we wanted was a recipe for how to best grill ribs. How to Cook Everything had some rib recipes, but nothing about grilling (that man’s into oven-roasting, apparently). Joy of Cooking has something (and, in the end, we’re following that recipe that didn’t seem like much of a recipe). Beyond that, though, our books utterly failed us.

Boo.… Continue reading →

Bayless’s recipe this week.

Cover of Fiesta at Rick's by Rick Bayless

“Proc 4 grlc,6T ancho,4t sugr&peppr,5t salt,1t oreg,½t cumin.Rub 4 slb ribs;ovrnite.Bake 300 75 min.Blend:7oz chiptles&3/4c honey.Grill;glaze”

Okay, if you count what’s between the quotation marks, you’ll find that it’s 140 characters long. Obviously, there’s enough there to get a sense of what is supposed to be going on, but is there actually enough there for a total beginner at grilling and ribs to make dinner? And, given the quantity of baby back ribs there, is there enough going on that a total beginner can make it for dinner WITH COMPANY?

Somehow, I suspect that wasn’t entirely Rick Bayless’s intent, but I do hope he gives some points for optimism. However, given the cookbook giveaway is for “Fiesta at Rick’s”, maybe it was his intent, and I played right into his sneaky hands!… Continue reading →


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Chrzan, K LPerformance of Composite Insulators Without Sheds under Light PollutionThe test of insulators without sheds or with small sheds number at Glogow pollution station was described. The performance of insulators with different leakage distance and position (horizontal or vertical) under light pollution is studied. The flashovers, leakage currents and surface erosion were monitored. The 75 kV test voltage is 4 kV higher than the maximum phase to ground voltage of 110 kV system. After long term testing the leakage distance of 80 cm was as withstand distance for horizontal rods and 100 cm for vertical rods. For practical proposes the security factor of 1.3 should be taken into account. Finally, under light pollution the 110 kV composite insulator, in horizontal position could be installed in the form of 100 cm long rods without sheds and vertical insulators with terminal distance of 100 em should have at least two additional sheds.pollutionflashoverinsulator20072007-08-01


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Hadoop Ecosystem Components contd…(Tutorial Day 6)

So continuing the old post, here we will discuss some more components of Hadoop ecosystem.

Data Integration or ETL Components of Hadoop Ecosystem

Sqoop (SQL-to-Hadoop) is a big data tool that offers the capability to extract bulk data from non-Hadoop  or relational databases (like MySQL, Oracle,Teradata, Postgre) , transform the data into a form usable by Hadoop, and then load the data into HDFS, Hbase or Hive also. This process is similar to Extract, Transform, and Load.It parallelizes data transfer for fast performance, copies data quickly from external system to Hadoop & makes data analysis more efficient.

It’s batch oriented and not suitable for low latency interactive queries. It provides a scalable processing environment for both structured and non-structured data.

Sqoop Import

Sqoop Export

Sqoop Use Case-

Coupons.com , Apollo Group uses Sqoop component of the Hadoop ecosystem to enable transmission of data between Hadoop & data warehouse .
  • Flume-

Apache Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of streaming or log files data into the Hadoop Distributed File System (HDFS).  It is used for collecting data from its origin and sending it back to the resting location (HDFS).Flume accomplishes this by outlining data flows that consist of 3 primary structures channels, sources and sinks. The processes that run the dataflow with flume are known as agents and the bits of data that flow via flume are known as events.

Flume helps to collect data from a variety of sources, like logs, jms, Directory etc.
Multiple flume agents can be configured to collect high volume of data.
It scales horizontally & is stream oriented.It provides high throughput and low latency.It is fault tolerant.

Both Sqoop and Flume, pull the data from the source and push it to the sink. The main difference is Flume is event driven, while Sqoop is not.

Flume Use Case –

Twitter source connects through the streaming API and continuously downloads the tweets (called as events). These tweets are converted into JSON format and sent to the downstream Flume sinks for further analysis of tweets and retweets to engage users on Twitter.
Goibibo uses Flume to transfer logs from production system to HDFS.

Data Storage Component of Hadoop Ecosystem

Hbase is an open source, distributed, sorted map model.Its a column store-based NoSQL database solution & is similar to Google’s BigTable framework.It supports random reads and also batch computations using MapReduce. With HBase NoSQL database enterprise can create large tables with millions of rows and columns on hardware machine. The best practice to use HBase is when there is a requirement for random ‘read or write’ access to big datasets. HBase’s important advantage is that it supports updates on larger tables and faster lookup. The HBase data store supports linear and modular scaling. HBase stores data as a multidimensional map and is distributed. HBase operations are all MapReduce tasks that run in a parallel manner.

Its well integrated with Pig/Hive/Sqoop. It is consistent and partition tolerant system in CAP theorem.

HBase Use Case-

Facebook is one the largest users of HBase with its messaging platform built on top of HBase in 2010.

Apache Cassandra is a free and open-source distributed database management system designed to handle large amounts of data across many commodity servers.This database is the right choice when you need scalability and high availability without compromising performance. Linear scalability and proven fault-tolerance on commodity hardware or cloud infrastructure make it the perfect platform for mission-critical data.Cassandra’s support for replicating across multiple data-centers is best-in-class, providing lower latency for your users and the peace of mind of knowing that you can survive regional outages.

Use Cases:

For Cassandra, Twitter is an excellent example. We know that, like most sites, user information (screen name, password, email address, etc), is kept for everyone and that those entries are linked to one another to map friends and followers. And, it wouldn’t be Twitter if it weren’t storing tweets, which in addition to the 140 characters of text are also associated with meta-data like timestamp and the unique id that we see in the URLs.

Monitoring, Management and Orchestration Components of Hadoop Ecosystem- Oozie and Zookeeper

  • Oozie-

Oozie is a workflow scheduler where the workflows are expressed as Directed Acyclic Graphs. Oozie runs in a Java servlet container Tomcat and makes use of a database to store all the running workflow instances, their states ad variables along with the workflow definitions to manage Hadoop jobs (MapReduce, Sqoop, Pig and Hive).The workflows in Oozie are executed based on data and time dependencies.

Oozie Use Case:

The American video game publisher Riot Games uses Hadoop and the open source tool Oozie to understand  the player experience.

  • Zookeeper-

Zookeeper is the king of coordination and provides simple, fast, reliable and ordered operational services for a Hadoop cluster. Zookeeper is responsible for synchronization service, distributed configuration service and for providing a naming registry for distributed systems.

Zookeeper Use Case-

Here is the recorded session from the IBM Certified Hadoop Developer Course at DeZyre about the components of Hadoop Ecosystem –
Several other common Hadoop ecosystem components include: Avro, Cassandra, Chukwa, Mahout, HCatalog, Ambari and Hama. By implementing Hadoop using one or more of the Hadoop ecosystem components, users can personalize their big data experience to meet the changing business requirements. The demand for big data analytics will make the elephant stay in the big data room for quite some time.

Data Serialisation (Data Interchange Protocols)

AVRO: Apache Avro is a language-neutral data serialization system, developed by  Apache Hadoop.Data serialization is a mechanism to translate data in computer environment (like memory buffer, data structures or object state) into binary or textual form that can be transported over network or stored in some persistent storage media.Java and Hadoop provides serialization APIs, which are java based, but Avro is not only language independent but also it is schema-based.Once the data is transported over network or retrieved from the persistent storage, it needs to be deserialized again. Serialization is termed as marshalling and deserialization is termed as unmarshalling.

Avro uses JSON format to declare the data structures. Presently, it supports languages such as Java, C, C++, C#, Python, and Ruby.Avro has a schema-based system. A language-independent schema is associated with its read and write operations.

Like Avro, there are other serialization mechanisms in Hadoop such as Sequence Files, Protocol Buffers, and Thrift.Avro creates a self-describing file named Avro Data File, in which it stores data along with its schema in the metadata section.Avro is also used in Remote Procedure Calls (RPCs). During RPC, client and server exchange schemas in the connection handshake.

To serialize Hadoop data, there are two ways −

  • You can use the Writable classes, provided by Hadoop’s native library.
  • You can also use Sequence Files which store the data in binary format.

The main drawback of these two mechanisms is that Writables and SequenceFiles have only a Java API and they cannot be written or read in any other language.

Therefore any of the files created in Hadoop with above two mechanisms cannot be read by any other third language, which makes Hadoop as a limited box. To address this drawback, Doug Cutting created Avro, which is a language independent data structure.

Use Case:

Content credit : http://www.tutorialspoint.com

Avro provides rich data structures. For example, you can create a record that contains an array, an enumerated type, and a sub record. These datatypes can be created in any language, can be processed in Hadoop, and the results can be fed to a third language.

 Thrift :

Thrift is a lightweight, language-independent software stack with an associated code generation mechanism for RPC. Thrift provides clean abstractions for data transport, data serialization, and application level processing. Thrift was originally developed by Facebook and now it is open sourced as an Apache project. Apache Thrift is a set of code-generation tools that allows developers to build RPC clients and servers by just defining the data types and service interfaces in a simple definition file. Given this file as an input, code is generated to build RPC clients and servers that communicate seamlessly across programming languages.

Thrift supports a variety of languages including C++, Java, Python, PHP, Ruby.

To learn more on Hadoop…keep on reading these tutorials…every day we try to get something new and interesting for all my readers !!

Now we will start learning all Ecosystem components in more detail. Click here to read about how MapReduce Algorithm works with an easy example.

Hadoop Ecosystem Components contd…(Tutorial Day 5)

Data Access Components of Hadoop Ecosystem

  • Pig-

Usage of Apache Pig:

Pig Use Case-

Code credit: Nick Dimiduk
  • Hive-

Hive Use Case-

Types of Databases in existing world

The capture and analyzing of data is typically performed by database management systems, otherwise known as DBMS’s. These types of database software systems are programmed in SQL. SQL (pronounced “ess-que-el”) stands for Structured Query Language. SQL is used to communicate with a database. According to ANSI (American National Standards Institute), it is the standard language for relational database management systems. The most common of all the different types of databases is Relational Databases.

Let’s learn now the different types of databases that exist in today’s world and how to use them in our work.

Types of Databases

  • Relational Databases: A relational database is a collection of data items organized as a set of formally-described tables from which data can be accessed or reassembled in many different ways without having to reorganize the database tables. The relational database was invented by E. F. Codd at IBM in 1970. Example are: PostgreSQL, SQLite, MySQL ,Oracle, Sysbase.
  • No SQLDatabases/Non-relational Databases : A NoSQL (originally referring to “non SQL”, “non relational” or “not only SQL”) database provides a mechanism for storage and retrieval of data which is modeled in means other than the tabular relations used in relational databases. NoSQL databases are increasingly used in big data and real-time web applications.NoSQL systems are also sometimes called “Not only SQL” to emphasize that they may support SQL-like query languages. Motivations for this approach include: simplicity of design, simpler “horizontal” scaling to clusters of machines (which is a problem for relational databases), and finer control over availability. The data structures used by NoSQL databases (e.g. key-value, columnar, graph, or document) are different from those used by default in relational databases, making some operations faster in NoSQL.

  • New SQL Databases: NewSQL is a term to describe a new group of databases that share much of the functionality of traditional SQL relational databases, while offering some of the benefits of NoSQL technologies.Like it provides, ACID transactional consistency of traditional operational databases; the familiarity and interactivity of SQL; and the scalability and speed of NoSQL.

Example to understand both above Databases:

Types and examples of NoSQL databases

There have been various approaches to classify NoSQL databases, each with different categories and subcategories, some of which overlap. What follows is a basic classification by data model, with examples:

  1. Key-Value Pair (KVP) Databases: Key-value (KV) stores use the associative array (also known as a map or dictionary) as their fundamental data model. In this model, data is represented as a collection of key-value pairs, such that each possible key appears at most once in the collection. The key-value model can be extended to a discretely ordered model that maintains keys in lexicographic order. This extension is computationally powerful, in that it can efficiently retrieve selective key e.g., InfinityDB, Oracle NoSQL Database and dbm.
  2. Document Databases: each document-oriented database implementation differs on the details of this definition, in general, they all assume that documents encapsulate and encode data (or information) in some standard formats or encodings. Encodings in use include XML and JSON. Documents are addressed in the database via a unique key that represents that document. One of the other defining characteristics of a document-oriented database is that in addition to the key lookup performed by a key-value store, the database offers an API or query language that retrieves documents based on their contents.

  • Collections
  • Tags
  • Non-visible metadata
  • Directory hierarchies

In short, Store documents or web pages, e.g.,MongoDB, Apache CouchDB

  1. Columnar Databases: Store data in columns, e.g., Hbase, SAP Hana
  2. Graph Databases: This kind of database is designed for data whose relations are well represented as a graph consisting of elements interconnected with a finite number of relations between them. The type of data could be social relations, public transport links, road maps or network topologies. Stores nodes and relationship, e.g., Neo4J, FlockDB
  3. Spatial Databases: For map and nevigational data, e.g.,OpenGEO, PortGIS, ArcSDE
  4. In-Memory Database (IMDB): All data in memory. For real time applications
  5. Cloud Databases: Any data that is run in a cloud using IAAS,VM Image, DAAS
            dbimages                                                                      Image courtesy: theWindowsclub.com
Advantages of NoSQL database:
  • Process data faster
  • Have simple data models to understand and execute
  • manage unstructured text